Agentic Engineering Workflow
Optimizes AI agent performance through eval-driven execution, task decomposition, and cost-aware model routing.
The Agentic Engineering skill transforms Claude into a high-performance autonomous developer by implementing a structured, research-first development lifecycle. It shifts the human role toward high-level quality and risk control while the AI manages implementation through an 'eval-first' loop. By breaking complex engineering tasks into 15-minute verifiable units and intelligently routing work between model tiers (Haiku, Sonnet, and Opus), this skill ensures more reliable code, minimized regressions, and significant cost savings during large-scale software development.
Key Features
01Eval-first execution loop to capture failure signatures before implementation.
02Strategic session management to maintain context and prevent debugging drift.
03Cost-aware model routing across Haiku, Sonnet, and Opus tiers based on task complexity.
04Granular task decomposition using the 15-minute unit rule for verifiable progress.
05137,500 GitHub stars
06Risk-focused review protocols prioritizing invariants, edge cases, and security assumptions.
Use Cases
01Optimizing development costs by automating boilerplate and classification with lower-tier models.
02Executing high-reliability refactors where regression testing is mandatory.
03Managing complex, multi-file feature implementations through structured agentic units.